High assurance AI unlocks bank adoption
The speakers argued that collaborative platforms focusing on high assurance AI are essential for regulated financial institutions to safely transition AI from experimentation to operational deployment.
The argument
The guest argued that banks face severe regulatory and operational risks from AI hallucinations, making explainability and auditability critical. By sharing finite compute and data resources in a common platform like Common AI, institutions can solve these trust and efficiency bottlenecks more effectively than building in isolation.
The thesis, stress-tested
✓ What validates it
- ✓Successful deployment of high assurance AI use cases by Barclays
- ✓Reduction in AI hallucinations in audited financial models
- ✓Regulatory approval of AI-driven underwriting and compliance models
▸ Risks discussed
- ▸Algorithmic bias persisting in trained models
- ▸Data privacy and protection failures
- ▸Severe operational or reputational fallout from model hallucinations
Hear it yourself
"The thesis is around Invisible Finance and more recently now, High Assurance AI. We have, a lot of investments as well in diverse founders. We're a big supporter of DE and I, and, we are based both here in London as well as New York. We recently cofounded, a platform called Common AI, which we launched in September last year, and I had the privilege of, coming to 11FS, to talk about that. So excited to be here today to talk more about the developments we've had in Common AI. Absolutely. Well, thank you so much for joining us. Yeah, we will be getting right into that very, very shortly. But next up on the panel, we also have Diana Camille Salmon, associate director at Fair for All Finance."
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